Enhancing Medical Image Recognition through Automated Neural Network Architecture Optimization using Neuroevolvenet

Medical image recognition is pivotal for accurate diagnostics and treatment planning. Traditional neural network architecture design methods are often time-consuming and suboptimal. To address this, an automated framework, NeuroEvolveNet is proposed for optimizing neural network architectures in medical image recognition. NeuroEvolveNet effectively investigates alternative architectural configurations for improved recognition accuracy by utilizing evolutionary algorithms. The difficulties of manual architectural design in medical imaging and the evolutionary strategy used by NeuroEvolveNet are outlined here. Our method is tailored for medical imaging data and is evaluated on a diverse medical image dataset. Through comprehensive experiments and various metrics, including accuracy, sensitivity, and specificity, the framework's effectiveness is demonstrated. Results consistently showcase NeuroEvolveNet's superiority over manual designs and existing methods. Evolved architectures exhibit higher accuracy, illustrating the potential of automated optimization in medical image recognition. Our study emphasizes benefits such as customization, efficiency, and adaptability in complex medical imaging tasks.This research contributes a novel method at the intersection of machine learning and healthcare, addressing challenges in medical image recognition. The integration of automated architecture optimization, as demonstrated by NeuroEvolveNet, holds promise for enhancing diagnostic accuracy and patient care in healthcare applications.